Pre-processing of the data

ls_preprocessed <- preprocess_rna(path_rnaseq = '/Users/senosam/Documents/Massion_lab/RNASeq_summary/rnaseq.RData', correct_batch = T, correct_gender = T)

Exploring data

Batch effect correction

print(ls_preprocessed$pbatch_bf)

print(ls_preprocessed$pgender_bf)

print(ls_preprocessed$pbatch_af)

print(ls_preprocessed$pgender_af)

DE analysis

gn <- as.character(ls_preprocessed$rna_all$Feature[which(ls_preprocessed$rna_all$Feature_gene_name =='PTGS2')])
DE_res <- DE_analysis(ls_preprocessed, 
           GeneBased=TRUE, 
           pDataBased=FALSE,
           NewCondition=FALSE,
           cond_nm= gn,
           reference = 'low', 
           correct_gender=TRUE,
           extremes_only=TRUE)
## Unlist done
## Labeling done
## Filtering done
## factor levels were dropped which had no samples
## Design done
## factor levels were dropped which had no samples
## Warning: Setting row names on a tibble is deprecated.
## vsd symbols done
## using pre-existing size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## -- replacing outliers and refitting for 1522 genes
## -- DESeq argument 'minReplicatesForReplace' = 7 
## -- original counts are preserved in counts(dds)
## estimating dispersions
## fitting model and testing
## DESeq done
## using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
## 
## Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
## See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
## Reference: https://doi.org/10.1093/bioinformatics/bty895
## res symbols done
## list done

DE results

heatmap_200(DE_res$res_df, DE_res$vsd_mat_sym, DE_res$meta_data, DE_res$pData_rnaseq)

x <- DE_res$res_df %>%
  arrange(desc(abs(log2FoldChange)))
rownames(x) <- make.names(x$symbol, unique = T)
k <- gn
x <- x[-which(x$gene %in%k),]
#head(x, 10)
vp <- volcano_plot(x, gene=NULL, p_title='PTGS2', pCutoff=0.001, FCcutoff=1.5)

List of genes differentially expressed (-1.5 > fold change > 1.5, pval<0.001)

vp_tb <- vp$data[which(vp$data$Sig == 'FC_P'),]
rownames(vp_tb) <- c(1:nrow(vp_tb))
kable(vp_tb)
baseMean log2FoldChange lfcSE stat pvalue padj gene symbol Sig lab xvals yvals
30.863992 -2.479060 0.3439049 -3.398115 0.0006785 0.0319640 ENSG00000088726.11 TMEM40 FC_P TMEM40 -2.479060 0.0006785
2964.903833 -2.207089 0.3797444 -5.472887 0.0000000 0.0001356 ENSG00000108602.13 ALDH3A1 FC_P ALDH3A1 -2.207089 0.0000000
15.439500 2.186333 0.3380666 6.398385 0.0000000 0.0000017 ENSG00000238939.1 snoU13 FC_P snoU13 2.186333 0.0000000
101.885165 -2.073900 0.3717441 -5.954557 0.0000000 0.0000144 ENSG00000227471.4 AKR1B15 FC_P AKR1B15 -2.073900 0.0000000
965.641295 2.048294 0.3423081 3.545670 0.0003916 0.0233105 ENSG00000130600.11 H19 FC_P H19 2.048294 0.0003916
705.858929 2.040664 0.3540554 5.952915 0.0000000 0.0000144 ENSG00000171246.5 NPTX1 FC_P NPTX1 2.040664 0.0000000
86.147620 -2.017125 0.3761486 -4.577180 0.0000047 0.0022361 ENSG00000196091.8 MYBPC1 FC_P MYBPC1 -2.017125 0.0000047
250.536264 1.972932 0.3800024 5.420838 0.0000001 0.0001387 ENSG00000144481.12 TRPM8 FC_P TRPM8 1.972932 0.0000001
18.120723 -1.971998 0.3771872 -5.205338 0.0000002 0.0002883 ENSG00000264785.1 RP11-311F12.2 FC_P RP11.311F12.2 -1.971998 0.0000002
221.303036 1.936225 0.3662064 6.089856 0.0000000 0.0000092 ENSG00000133055.4 MYBPH FC_P MYBPH 1.936225 0.0000000
39.977179 1.919878 0.3578523 5.438106 0.0000001 0.0001356 ENSG00000163873.5 GRIK3 FC_P GRIK3 1.919878 0.0000001
47.209055 -1.900883 0.3664444 -5.286592 0.0000001 0.0002400 ENSG00000117971.7 CHRNB4 FC_P CHRNB4 -1.900883 0.0000001
34.643169 -1.889957 0.3497089 -5.368104 0.0000001 0.0001628 ENSG00000133454.11 MYO18B FC_P MYO18B -1.889957 0.0000001
88.440914 1.859034 0.3454515 5.116616 0.0000003 0.0003917 ENSG00000263293.1 RP11-290H9.4 FC_P RP11.290H9.4 1.859034 0.0000003
68.508056 -1.814031 0.3413006 -5.397081 0.0000001 0.0001478 ENSG00000230524.4 COL6A4P1 FC_P COL6A4P1 -1.814031 0.0000001
4.183238 -1.788793 0.3783275 -4.851113 0.0000012 NA ENSG00000259555.1 RP11-335K5.2 FC_P RP11.335K5.2 -1.788793 0.0000012
9.923174 -1.778642 0.3798210 -4.609164 0.0000040 0.0020054 ENSG00000266670.1 RN7SL637P FC_P RN7SL637P -1.778642 0.0000040
769.238937 1.763002 0.3354016 5.457343 0.0000000 0.0001356 ENSG00000136244.7 IL6 FC_P IL6 1.763002 0.0000000
35.220211 -1.760101 0.3744582 -4.675206 0.0000029 0.0018824 ENSG00000167419.6 LPO FC_P LPO -1.760101 0.0000029
127.247817 -1.758937 0.3760631 -4.797951 0.0000016 0.0013119 ENSG00000147573.12 TRIM55 FC_P TRIM55 -1.758937 0.0000016
76.308310 1.751477 0.3695326 4.938820 0.0000008 0.0007747 ENSG00000171864.4 PRND FC_P PRND 1.751477 0.0000008
42.119413 -1.746615 0.3788470 -4.972110 0.0000007 0.0006994 ENSG00000204529.3 GUCY2EP FC_P GUCY2EP -1.746615 0.0000007
35.978596 1.736665 0.2768065 6.422754 0.0000000 0.0000017 ENSG00000273129.1 RP5-973M2.2 FC_P RP5.973M2.2 1.736665 0.0000000
812.764631 1.735449 0.3481507 4.104363 0.0000405 0.0072670 ENSG00000007908.11 SELE FC_P SELE 1.735449 0.0000405
40.424738 -1.720007 0.3738841 -4.589461 0.0000044 0.0021395 ENSG00000237810.3 CTD-2571E19.3 FC_P CTD.2571E19.3 -1.720007 0.0000044
57.975367 1.691321 0.3594510 4.610884 0.0000040 0.0020054 ENSG00000054803.3 CBLN4 FC_P CBLN4 1.691321 0.0000040
386.091419 -1.676048 0.3762493 -4.431439 0.0000094 0.0032601 ENSG00000243955.1 GSTA1 FC_P GSTA1 -1.676048 0.0000094
304.948367 -1.652732 0.3606650 -4.416394 0.0000100 0.0033872 ENSG00000171658.4 RP11-443P15.2 FC_P RP11.443P15.2 -1.652732 0.0000100
10.844419 -1.643394 0.3800087 -4.372455 0.0000123 0.0036919 ENSG00000163793.8 DNAJC5G FC_P DNAJC5G -1.643394 0.0000123
261.716752 -1.613595 0.3376515 -4.733409 0.0000022 0.0015713 ENSG00000174564.8 IL20RB FC_P IL20RB -1.613595 0.0000022
21.962206 -1.609315 0.3619317 -4.487156 0.0000072 0.0028131 ENSG00000215644.5 GCGR FC_P GCGR -1.609315 0.0000072
30.302262 1.604662 0.2948598 5.533832 0.0000000 0.0001282 ENSG00000249545.1 CTC-558O19.1 FC_P CTC.558O19.1 1.604662 0.0000000
457.989439 -1.587791 0.3304879 -5.023543 0.0000005 0.0005727 ENSG00000113924.7 HGD FC_P HGD -1.587791 0.0000005
573.891976 -1.587627 0.3443689 -4.470575 0.0000078 0.0029355 ENSG00000160211.11 G6PD FC_P G6PD -1.587627 0.0000078
4420.543243 -1.580947 0.3697534 -4.115958 0.0000386 0.0070693 ENSG00000187134.8 AKR1C1 FC_P AKR1C1 -1.580947 0.0000386
9.824994 1.577737 0.3709120 4.392523 0.0000112 0.0035468 ENSG00000252431.1 RNU6-1247P FC_P RNU6.1247P 1.577737 0.0000112
81.580956 1.575815 0.3665399 4.349913 0.0000136 0.0037911 ENSG00000141433.8 ADCYAP1 FC_P ADCYAP1 1.575815 0.0000136
40.121661 1.564765 0.3784804 4.211927 0.0000253 0.0054779 ENSG00000165606.4 DRGX FC_P DRGX 1.564765 0.0000253
10.846014 -1.563250 0.3751473 -4.081284 0.0000448 0.0075193 ENSG00000149742.5 SLC22A9 FC_P SLC22A9 -1.563250 0.0000448
20.669399 -1.546334 0.3529611 -4.532112 0.0000058 0.0025835 ENSG00000253379.1 RP11-1102P16.1 FC_P RP11.1102P16.1 -1.546334 0.0000058
14.343559 -1.537542 0.3739281 -4.324996 0.0000153 0.0040386 ENSG00000233080.2 CTA-714B7.5 FC_P CTA.714B7.5 -1.537542 0.0000153
4843.472295 1.536494 0.3480689 4.948693 0.0000007 0.0007644 ENSG00000104368.13 PLAT FC_P PLAT 1.536494 0.0000007
5.078966 -1.535966 0.3737345 -4.644839 0.0000034 0.0018824 ENSG00000258145.1 BX927168.3 FC_P BX927168.3 -1.535966 0.0000034
33.637824 1.534993 0.3606770 4.649579 0.0000033 0.0018824 ENSG00000270080.1 RP6-99M1.2 FC_P RP6.99M1.2 1.534993 0.0000033
6.026043 1.534644 0.3806103 3.953790 0.0000769 0.0101133 ENSG00000256870.2 SLC5A8 FC_P SLC5A8 1.534644 0.0000769
573.446918 1.528067 0.3223235 4.462010 0.0000081 0.0030206 ENSG00000259207.3 ITGB3 FC_P ITGB3 1.528067 0.0000081
132.344366 1.523856 0.3629211 4.155283 0.0000325 0.0063099 ENSG00000108342.8 CSF3 FC_P CSF3 1.523856 0.0000325
8.860518 -1.523608 0.3714336 -4.155370 0.0000325 0.0063099 ENSG00000261303.1 RP11-160C18.2 FC_P RP11.160C18.2 -1.523608 0.0000325
289.481825 -1.521638 0.3709542 -3.988755 0.0000664 0.0090801 ENSG00000249628.2 LINC00942 FC_P LINC00942 -1.521638 0.0000664
2.838738 -1.518618 0.3800038 -3.927524 0.0000858 NA ENSG00000221446.1 AC099805.1 FC_P AC099805.1 -1.518618 0.0000858
23.427723 -1.514773 0.3689689 -3.927944 0.0000857 0.0107002 ENSG00000219700.1 PTCHD3P3 FC_P PTCHD3P3 -1.514773 0.0000857
42.741613 1.514567 0.3694188 5.189432 0.0000002 0.0002883 ENSG00000252236.1 SNORA26 FC_P SNORA26 1.514567 0.0000002
21.315531 -1.514550 0.3790428 -3.840276 0.0001229 0.0126920 ENSG00000175329.8 ISX FC_P ISX -1.514550 0.0001229
1200.534861 1.513667 0.3714697 4.473439 0.0000077 0.0029301 ENSG00000196611.4 MMP1 FC_P MMP1 1.513667 0.0000077
4456.952479 1.513016 0.3026534 5.465515 0.0000000 0.0001356 ENSG00000119508.13 NR4A3 FC_P NR4A3 1.513016 0.0000000
1600.004739 -1.511491 0.2998332 -4.895326 0.0000010 0.0008455 ENSG00000159228.8 CBR1 FC_P CBR1 -1.511491 0.0000010
66.282187 -1.504971 0.3766435 -4.121835 0.0000376 0.0069794 ENSG00000254632.1 RP11-21L23.4 FC_P RP11.21L23.4 -1.504971 0.0000376
492.343147 -1.503084 0.3317930 -4.496096 0.0000069 0.0027300 ENSG00000181652.14 ATG9B FC_P ATG9B -1.503084 0.0000069

Pathway enrichment analysis fGSEA

Low PTGS2 is the reference. When PTGS2 is high, pathways shown below are up- or down- regulated

fgsea_res <- fgsea_analysis(DE_res)
## `summarise()` ungrouping output (override with `.groups` argument)
## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm = 1000): There are ties in the preranked stats (0.04% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm = 1000): There are ties in the preranked stats (0.04% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm = 1000): There are ties in the preranked stats (0.04% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm = 1000): There are ties in the preranked stats (0.04% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm = 1000): There are ties in the preranked stats (0.04% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm = 1000): There are ties in the preranked stats (0.04% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm = 1000): There are ties in the preranked stats (0.04% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm = 1000): There are ties in the preranked stats (0.04% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.

## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm = 1000): There are ties in the preranked stats (0.04% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
fgp <- fgsea_plot(fgsea_res$res_hm, pathways_title='Hallmark', condition_name='PTGS2 low vs high')

kable(fgp)
pathway pval padj ES NES nMoreExtreme size state
HALLMARK_TNFA_SIGNALING_VIA_NFKB 0.0029586 0.0070442 0.6718790 3.434948 0 195 up
HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION 0.0028986 0.0070442 0.6070484 3.085146 0 189 up
HALLMARK_INFLAMMATORY_RESPONSE 0.0029070 0.0070442 0.5254339 2.683342 0 193 up
HALLMARK_UV_RESPONSE_DN 0.0027322 0.0070442 0.4620131 2.253674 0 136 up
HALLMARK_TGF_BETA_SIGNALING 0.0024450 0.0070442 0.5363940 2.174837 0 54 up
HALLMARK_ANDROGEN_RESPONSE 0.0025381 0.0070442 0.4704808 2.170916 0 96 up
HALLMARK_KRAS_SIGNALING_UP 0.0029240 0.0070442 0.4210727 2.143357 0 190 up
HALLMARK_IL6_JAK_STAT3_SIGNALING 0.0024450 0.0070442 0.4581458 2.060534 0 82 up
HALLMARK_APOPTOSIS 0.0028490 0.0070442 0.4034059 1.993316 0 155 up
HALLMARK_ANGIOGENESIS 0.0023641 0.0070442 0.5333233 1.966040 0 35 up
HALLMARK_IL2_STAT5_SIGNALING 0.0029240 0.0070442 0.3818565 1.943737 0 190 up
HALLMARK_PROTEIN_SECRETION 0.0024876 0.0070442 0.4154849 1.893443 0 89 up
HALLMARK_FATTY_ACID_METABOLISM 0.0015432 0.0070442 -0.3946258 -1.830420 0 147 down
HALLMARK_XENOBIOTIC_METABOLISM 0.0015198 0.0070442 -0.3755563 -1.801707 0 188 down
HALLMARK_HYPOXIA 0.0027701 0.0070442 0.3520276 1.784744 0 180 up
HALLMARK_KRAS_SIGNALING_DN 0.0015625 0.0070442 -0.3736344 -1.775946 0 179 down
HALLMARK_ALLOGRAFT_REJECTION 0.0028818 0.0070442 0.3329580 1.693405 0 187 up
HALLMARK_GLYCOLYSIS 0.0015198 0.0070442 -0.3449329 -1.654794 0 188 down
HALLMARK_COMPLEMENT 0.0029070 0.0070442 0.3115283 1.590945 0 193 up
HALLMARK_MYC_TARGETS_V1 0.0028986 0.0070442 0.3121833 1.586581 0 189 up
HALLMARK_COAGULATION 0.0027322 0.0070442 0.3205127 1.547631 0 127 up
HALLMARK_INTERFERON_ALPHA_RESPONSE 0.0032949 0.0074884 -0.3704954 -1.591107 1 93 down
HALLMARK_INTERFERON_GAMMA_RESPONSE 0.0057971 0.0126024 0.2858225 1.463917 1 194 up
HALLMARK_E2F_TARGETS 0.0060698 0.0126454 -0.3057234 -1.466908 3 191 down
HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY 0.0068376 0.0136752 -0.4506941 -1.682955 3 46 down
HALLMARK_CHOLESTEROL_HOMEOSTASIS 0.0096154 0.0184911 0.3778151 1.622887 3 69 up 
fgp <- fgsea_plot(fgsea_res$res_c1, pathways_title='C1 positional genes', condition_name='PTGS2 low vs high')

kable(fgp)
pathway pval padj ES NES nMoreExtreme size state
chr14q22 0.0024450 0.0089866 0.6480918 2.765761 0 62 up
chr17q25 0.0014925 0.0089866 -0.5605161 -2.703600 0 202 down
chr14q11 0.0014793 0.0089866 -0.5576148 -2.676253 0 196 down
chr2q33 0.0025907 0.0089866 0.5664097 2.666090 0 103 up
chr3q12 0.0023202 0.0089866 0.7217473 2.632674 0 30 up
chr5q22 0.0023364 0.0089866 0.6585974 2.544725 0 38 up
chr16p13 0.0014881 0.0089866 -0.5038103 -2.504243 0 272 down
chr5q11 0.0023529 0.0089866 0.6180480 2.471369 0 45 up
chr15q21 0.0026042 0.0089866 0.5242948 2.464045 0 102 up
chr16p11 0.0015924 0.0089866 -0.5485333 -2.450083 0 120 down
chr5p13 0.0025253 0.0089866 0.5180763 2.325249 0 84 up
chr8q24 0.0014970 0.0089866 -0.4854562 -2.313309 0 185 down
chr1p36 0.0013774 0.0089866 -0.4452136 -2.308809 0 395 down
chr13q22 0.0022936 0.0089866 0.6562163 2.294614 0 26 up
chr13q32 0.0024570 0.0089866 0.5285869 2.287202 0 67 up
chr9q33 0.0025974 0.0089866 0.4853644 2.284606 0 104 up
chr11q13 0.0014577 0.0089866 -0.4526367 -2.270298 0 290 down
chr2q31 0.0026110 0.0089866 0.4906042 2.261162 0 94 up
chr2p16 0.0023923 0.0089866 0.5483907 2.247815 0 51 up
chr2q32 0.0023981 0.0089866 0.5184409 2.228915 0 63 up
chr1q44 0.0024390 0.0089866 0.5490340 2.215533 0 48 up
chr8p23 0.0025063 0.0089866 0.4896963 2.198111 0 83 up
chr8p22 0.0022624 0.0089866 0.6335003 2.190745 0 25 up
chr7p11 0.0018315 0.0089866 -0.7038405 -2.171840 0 17 down
chr13q13 0.0023585 0.0089866 0.5372955 2.156761 0 46 up
chr12q21 0.0024213 0.0089866 0.4972314 2.154743 0 66 up
chr18p11 0.0025907 0.0089866 0.4664890 2.149495 0 93 up
chr11q14 0.0025126 0.0089866 0.4835247 2.130963 0 76 up
chr3p14 0.0025063 0.0089866 0.4744171 2.129527 0 83 up
chr13q33 0.0023202 0.0089866 0.5604843 2.127107 0 35 up 
fgp <- fgsea_plot(fgsea_res$res_c2, pathways_title='C2 curated genes', condition_name='PTGS2 low vs high')

kable(fgp)
pathway pval padj ES NES nMoreExtreme size state
PHONG_TNF_TARGETS_UP 0.0023753 0.0191175 0.7491497 3.178509 0 62 up
ANASTASSIOU_MULTICANCER_INVASIVENESS_SIGNATURE 0.0023753 0.0191175 0.7340077 3.114265 0 62 up
CHEN_HOXA5_TARGETS_9HR_UP 0.0029326 0.0191175 0.5944775 3.074291 0 211 up
ZWANG_CLASS_3_TRANSIENTLY_INDUCED_BY_EGF 0.0030030 0.0191175 0.5855923 3.038171 0 219 up
PICCALUGA_ANGIOIMMUNOBLASTIC_LYMPHOMA_DN 0.0025641 0.0191175 0.6237117 3.024216 0 128 up
BROCKE_APOPTOSIS_REVERSED_BY_IL6 0.0026596 0.0191175 0.6128985 2.992494 0 140 up
KEGG_RIBOSOME 0.0024096 0.0191175 0.6687799 2.988530 0 81 up
TURASHVILI_BREAST_LOBULAR_CARCINOMA_VS_DUCTAL_NORMAL_UP 0.0024213 0.0191175 0.6959614 2.972521 0 66 up
UZONYI_RESPONSE_TO_LEUKOTRIENE_AND_THROMBIN 0.0024213 0.0191175 0.8018658 2.971768 0 34 up
PECE_MAMMARY_STEM_CELL_DN 0.0025707 0.0191175 0.6026885 2.937909 0 132 up
REACTOME_RESPONSE_OF_EIF2AK4_GCN2_TO_AMINO_ACID_DEFICIENCY 0.0024631 0.0191175 0.6396325 2.926438 0 94 up
REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE 0.0024570 0.0191175 0.6205736 2.898645 0 104 up
REACTOME_EUKARYOTIC_TRANSLATION_ELONGATION 0.0023810 0.0191175 0.6409805 2.895107 0 86 up
LINDGREN_BLADDER_CANCER_CLUSTER_2A_DN 0.0025575 0.0191175 0.5957315 2.894720 0 129 up
WAMUNYOKOLI_OVARIAN_CANCER_LMP_DN 0.0028571 0.0191175 0.5684874 2.894282 0 184 up
REACTOME_EUKARYOTIC_TRANSLATION_INITIATION 0.0025126 0.0191175 0.6114179 2.885597 0 112 up
VECCHI_GASTRIC_CANCER_ADVANCED_VS_EARLY_UP 0.0027855 0.0191175 0.5716292 2.852762 0 161 up
DAUER_STAT3_TARGETS_UP 0.0023202 0.0191175 0.7096314 2.823669 0 46 up
NIKOLSKY_BREAST_CANCER_16P13_AMPLICON 0.0016920 0.0191175 -0.6373712 -2.817941 0 105 down
AMIT_SERUM_RESPONSE_60_MCF10A 0.0023202 0.0191175 0.6770909 2.817641 0 55 up
NAGASHIMA_NRG1_SIGNALING_UP 0.0028490 0.0191175 0.5604463 2.800306 0 167 up
REACTOME_NONSENSE_MEDIATED_DECAY_NMD 0.0024096 0.0191175 0.5907164 2.793886 0 108 up
ZHENG_FOXP3_TARGETS_IN_THYMUS_UP 0.0028736 0.0191175 0.5448709 2.782158 0 189 up
IKEDA_MIR30_TARGETS_UP 0.0025000 0.0191175 0.5828889 2.759508 0 113 up
SAKAI_CHRONIC_HEPATITIS_VS_LIVER_CANCER_UP 0.0023866 0.0191175 0.6159585 2.759291 0 80 up
ONDER_CDH1_TARGETS_1_DN 0.0027174 0.0191175 0.5501922 2.755134 0 159 up
GROSS_HYPOXIA_VIA_ELK3_DN 0.0026178 0.0191175 0.5572989 2.751890 0 145 up
FALVELLA_SMOKERS_WITH_LUNG_CANCER 0.0023641 0.0191175 0.6189870 2.743006 0 75 up
FLOTHO_PEDIATRIC_ALL_THERAPY_RESPONSE_UP 0.0022883 0.0191175 0.6804240 2.738318 0 49 up
CROONQUIST_STROMAL_STIMULATION_UP 0.0022936 0.0191175 0.6569458 2.722766 0 54 up 
fgp <- fgsea_plot(fgsea_res$res_c3, pathways_title='C3 regulatory target genes', condition_name='PTGS2 low vs high')

kable(fgp)
pathway pval padj ES NES nMoreExtreme size state
MIR3973 0.0029326 0.0092382 0.5470727 2.904792 0 244 up
MIR3617_5P 0.0029412 0.0092382 0.5445195 2.887844 0 240 up
MIR641 0.0028902 0.0092382 0.5410508 2.877904 0 239 up
MIR576_5P 0.0031646 0.0092382 0.5231541 2.863549 0 315 up
MIR410_3P 0.0031250 0.0092382 0.5177129 2.818520 0 308 up
MIR4263 0.0028902 0.0092382 0.5282944 2.810051 0 239 up
MIR1252_3P 0.0031646 0.0092382 0.5184203 2.793553 0 281 up
MIR32_3P 0.0031348 0.0092382 0.5115229 2.788314 0 310 up
MIR7844_5P 0.0028902 0.0092382 0.5715168 2.787608 0 141 up
MIR145_5P 0.0031447 0.0092382 0.5154118 2.786194 0 287 up
MIR195_3P 0.0029155 0.0092382 0.5332156 2.775448 0 214 up
CTTGTAT_MIR381 0.0028653 0.0092382 0.5421413 2.774851 0 187 up
MIR5195_3P 0.0031746 0.0092382 0.5138148 2.770305 0 292 up
MIR16_2_3P 0.0029070 0.0092382 0.5286843 2.750729 0 213 up
TAATGTG_MIR323 0.0028902 0.0092382 0.5567339 2.749844 0 152 up
ACTGAAA_MIR30A3P_MIR30E3P 0.0028736 0.0092382 0.5345510 2.736554 0 190 up
ATGTTAA_MIR302C 0.0029412 0.0092382 0.5165642 2.728647 0 231 up
MIR888_5P 0.0029155 0.0092382 0.5198984 2.727298 0 222 up
MIR372_5P 0.0030120 0.0092382 0.5179592 2.725813 0 230 up
ATTACAT_MIR3803P 0.0027778 0.0092382 0.5935977 2.725732 0 96 up
MIR4789_3P 0.0031646 0.0092382 0.4978131 2.725435 0 318 up
MIR518A_5P_MIR527 0.0029586 0.0092382 0.5061683 2.687815 0 242 up
MIR545_5P 0.0031949 0.0092382 0.4958886 2.681790 0 296 up
MIR548AV_5P_MIR548K 0.0031847 0.0092382 0.4965367 2.676839 0 282 up
MIR34C_3P 0.0025063 0.0092382 0.6273882 2.673880 0 62 up
MIR8054 0.0031847 0.0092382 0.4937170 2.661637 0 282 up
MIR4760_5P 0.0028169 0.0092382 0.5190253 2.641164 0 179 up
MIR7853_5P 0.0029851 0.0092382 0.5118496 2.638307 0 206 up
CATTTCA_MIR203 0.0031546 0.0092382 0.4897187 2.630626 0 272 up
MIR586 0.0029851 0.0092382 0.4926144 2.627582 0 258 up 
fgp <- fgsea_plot(fgsea_res$res_c4, pathways_title='C4 cancer', condition_name='PTGS2 low vs high')

kable(fgp)
pathway pval padj ES NES nMoreExtreme size state
GNF2_PTX3 0.0025641 0.0175245 0.7760591 2.946426 0 35 up
GNF2_MMP1 0.0025510 0.0175245 0.7942854 2.928803 0 31 up
GNF2_ST13 0.0026385 0.0175245 0.6885203 2.896663 0 58 up
GNF2_DAP3 0.0027933 0.0175245 0.5988357 2.822404 0 110 up
GNF2_EIF3S6 0.0027933 0.0175245 0.5983728 2.820222 0 110 up
GNF2_RBBP6 0.0026525 0.0175245 0.6455645 2.816427 0 68 up
GNF2_TPT1 0.0025641 0.0175245 0.7123200 2.712772 0 36 up
MORF_TPT1 0.0027701 0.0175245 0.5823326 2.676855 0 92 up
GCM_TPT1 0.0026596 0.0175245 0.6309535 2.671192 0 59 up
MODULE_239 0.0027701 0.0175245 0.5363332 2.538283 0 111 up
MORF_JUND 0.0025773 0.0175245 0.5938818 2.509412 0 55 up
GCM_CSNK1A1 0.0025840 0.0175245 0.6637386 2.497259 0 34 up
GNF2_CDC20 0.0016129 0.0175245 -0.6465991 -2.493244 0 53 down
GNF2_PPP6C 0.0025641 0.0175245 0.6556029 2.489096 0 35 up
GNF2_FBL 0.0029326 0.0175245 0.5094511 2.478444 0 133 up
GNF2_CCNB2 0.0016234 0.0175245 -0.6396793 -2.469800 0 54 down
MODULE_332 0.0024570 0.0175245 0.6176494 2.467339 0 44 up
GNF2_TST 0.0015773 0.0175245 -0.5710539 -2.464096 0 95 down
GNF2_CDH11 0.0025575 0.0175245 0.7201195 2.462748 0 25 up
MORF_ACTG1 0.0029326 0.0175245 0.5093717 2.455888 0 127 up
GNF2_PCNA 0.0016026 0.0175245 -0.6142481 -2.454073 0 65 down
GCM_RAB10 0.0029155 0.0175245 0.4838825 2.445160 0 165 up
GNF2_GSTM1 0.0015649 0.0175245 -0.5600549 -2.443284 0 101 down
GNF2_CCNA2 0.0016026 0.0175245 -0.6099155 -2.436763 0 65 down
GNF2_ESPL1 0.0016340 0.0175245 -0.6894501 -2.422100 0 35 down
GCM_ZNF198 0.0027701 0.0175245 0.5077790 2.403145 0 111 up
MODULE_29 0.0025510 0.0175245 0.6916385 2.399001 0 26 up
GCM_NPM1 0.0028169 0.0175245 0.5074841 2.382291 0 104 up
GCM_HBP1 0.0026316 0.0175245 0.5580037 2.381155 0 61 up
MORF_NPM1 0.0029240 0.0175245 0.4752601 2.364476 0 149 up 
fgp <- fgsea_plot(fgsea_res$res_c5, pathways_title='C5 GO genes', condition_name='PTGS2 low vs high')

kable(fgp)
pathway pval padj ES NES nMoreExtreme size state 
fgp <- fgsea_plot(fgsea_res$res_c6, pathways_title='C6 oncogenic', condition_name='PTGS2 low vs high')

kable(fgp)
pathway pval padj ES NES nMoreExtreme size state
PIGF_UP.V1_UP 0.0029762 0.0114379 0.5753524 2.988685 0 181 up
EGFR_UP.V1_UP 0.0029940 0.0114379 0.5494265 2.849799 0 180 up
VEGF_A_UP.V1_DN 0.0029499 0.0114379 0.5179745 2.698541 0 184 up
CSR_EARLY_UP.V1_UP 0.0028090 0.0114379 0.5059503 2.530878 0 139 up
HOXA9_DN.V1_UP 0.0029940 0.0114379 0.4711044 2.413539 0 170 up
HINATA_NFKB_IMMU_INF 0.0022883 0.0114379 0.8305516 2.404089 0 14 up
BMI1_DN_MEL18_DN.V1_UP 0.0028090 0.0114379 0.4664368 2.312802 0 135 up
SIRNA_EIF4GI_DN 0.0025907 0.0114379 0.4922075 2.242667 0 88 up
ATF2_UP.V1_DN 0.0030211 0.0114379 0.4173613 2.142560 0 171 up
PRC2_EED_UP.V1_DN 0.0014815 0.0114379 -0.4511859 -2.135430 0 179 down
PDGF_UP.V1_UP 0.0028986 0.0114379 0.4345148 2.131017 0 133 up
TGFB_UP.V1_UP 0.0030675 0.0114379 0.4141101 2.128501 0 176 up
BCAT_BILD_ET_AL_DN 0.0023753 0.0114379 0.5238755 2.089688 0 45 up
RPS14_DN.V1_UP 0.0029326 0.0114379 0.4017856 2.088159 0 182 up
CAMP_UP.V1_DN 0.0029851 0.0114379 0.4011555 2.086744 0 186 up
ERBB2_UP.V1_DN 0.0029326 0.0114379 0.3924639 2.039712 0 182 up
E2F1_UP.V1_DN 0.0030675 0.0114379 0.3929226 2.019599 0 176 up
KRAS.50_UP.V1_UP 0.0023753 0.0114379 0.5052717 2.015479 0 45 up
SRC_UP.V1_UP 0.0015361 0.0114379 -0.4361672 -2.008377 0 148 down
RAF_UP.V1_UP 0.0030581 0.0114379 0.3845223 1.987339 0 179 up
LTE2_UP.V1_DN 0.0029326 0.0114379 0.3775259 1.962077 0 182 up
KRAS.KIDNEY_UP.V1_UP 0.0027701 0.0114379 0.3983047 1.959377 0 130 up
EIF4E_DN 0.0025126 0.0114379 0.4228544 1.957938 0 94 up
BMI1_DN.V1_UP 0.0028986 0.0114379 0.3974161 1.949072 0 133 up
RELA_DN.V1_UP 0.0028329 0.0114379 0.3902186 1.918080 0 131 up
KRAS.300_UP.V1_UP 0.0027701 0.0114379 0.3873041 1.905262 0 130 up
ATF2_S_UP.V1_DN 0.0030211 0.0114379 0.3701727 1.899403 0 172 up
KRAS.DF.V1_UP 0.0030864 0.0114379 0.3654774 1.885511 0 178 up
NFE2L2.V2 0.0013245 0.0114379 -0.3509105 -1.823915 0 395 down
KRAS.LUNG.BREAST_UP.V1_UP 0.0027397 0.0114379 0.3728237 1.818569 0 124 up 
fgp <- fgsea_plot(fgsea_res$res_c7, pathways_title='C7 immunologic', condition_name='PTGS2 low vs high')

kable(fgp)
pathway pval padj ES NES nMoreExtreme size state
GSE9988_LOW_LPS_VS_CTRL_TREATED_MONOCYTE_UP 0.0028571 0.01013 0.6626050 3.353794 0 178 up
GSE9988_LOW_LPS_VS_VEHICLE_TREATED_MONOCYTE_UP 0.0028490 0.01013 0.6476534 3.270482 0 175 up
GSE14769_UNSTIM_VS_80MIN_LPS_BMDM_DN 0.0029499 0.01013 0.6270112 3.208241 0 191 up
GSE9988_LPS_VS_CTRL_TREATED_MONOCYTE_UP 0.0028490 0.01013 0.6315690 3.189260 0 175 up
GSE14769_UNSTIM_VS_60MIN_LPS_BMDM_DN 0.0029240 0.01013 0.6206065 3.187954 0 192 up
GSE9988_LPS_VS_VEHICLE_TREATED_MONOCYTE_UP 0.0028090 0.01013 0.6278268 3.176728 0 176 up
GSE30971_CTRL_VS_LPS_STIM_MACROPHAGE_WBP7_KO_4H_UP 0.0027933 0.01013 0.6155783 3.142757 0 181 up
GSE9988_ANTI_TREM1_VS_ANTI_TREM1_AND_LPS_MONOCYTE_DN 0.0028329 0.01013 0.6165688 3.114530 0 172 up
GSE29617_CTRL_VS_DAY7_TIV_FLU_VACCINE_PBMC_2008_UP 0.0028329 0.01013 0.6123084 3.112872 0 180 up
GSE30971_CTRL_VS_LPS_STIM_MACROPHAGE_WBP7_KO_2H_UP 0.0028571 0.01013 0.6108320 3.094866 0 179 up
GSE21678_WT_VS_FOXO1_FOXO3_KO_TREG_UP 0.0028490 0.01013 0.6032215 3.046112 0 175 up
GSE42021_TREG_PLN_VS_CD24INT_TREG_THYMUS_UP 0.0028653 0.01013 0.5868058 3.033942 0 195 up
GSE9988_ANTI_TREM1_AND_LPS_VS_CTRL_TREATED_MONOCYTES_UP 0.0028249 0.01013 0.5888956 2.979970 0 177 up
GSE369_PRE_VS_POST_IL6_INJECTION_SOCS3_KO_LIVER_UP 0.0028329 0.01013 0.5848084 2.973066 0 180 up
GSE30971_CTRL_VS_LPS_STIM_MACROPHAGE_WBP7_HET_2H_UP 0.0028571 0.01013 0.5758679 2.917715 0 179 up
GSE27434_WT_VS_DNMT1_KO_TREG_DN 0.0029586 0.01013 0.5675373 2.910922 0 193 up
GSE9988_ANTI_TREM1_VS_CTRL_TREATED_MONOCYTES_UP 0.0028090 0.01013 0.5681529 2.904666 0 183 up
GSE12392_IFNAR_KO_VS_IFNB_KO_CD8_NEG_SPLEEN_DC_UP 0.0028736 0.01013 0.5690880 2.903194 0 184 up
GSE30971_WBP7_HET_VS_KO_MACROPHAGE_2H_LPS_STIM_DN 0.0029412 0.01013 0.5680119 2.902387 0 189 up
GSE29617_CTRL_VS_TIV_FLU_VACCINE_PBMC_2008_UP 0.0028409 0.01013 0.5708043 2.879920 0 171 up
GSE14769_UNSTIM_VS_40MIN_LPS_BMDM_DN 0.0029070 0.01013 0.5583699 2.877856 0 194 up
GSE9988_ANTI_TREM1_VS_VEHICLE_TREATED_MONOCYTES_UP 0.0028090 0.01013 0.5663335 2.865580 0 176 up
GSE9988_ANTI_TREM1_VS_LOW_LPS_MONOCYTE_DN 0.0028090 0.01013 0.5565787 2.845193 0 182 up
GSE14769_UNSTIM_VS_120MIN_LPS_BMDM_DN 0.0028736 0.01013 0.5487134 2.835748 0 197 up
GSE2706_UNSTIM_VS_2H_LPS_DC_DN 0.0028090 0.01013 0.5568357 2.817522 0 176 up
GSE45365_NK_CELL_VS_CD11B_DC_DN 0.0029070 0.01013 0.5440801 2.804206 0 194 up
GSE9988_ANTI_TREM1_VS_LPS_MONOCYTE_DN 0.0028090 0.01013 0.5473705 2.798121 0 182 up
GSE37605_FOXP3_FUSION_GFP_VS_IRES_GFP_TREG_C57BL6_DN 0.0028409 0.01013 0.5543919 2.792489 0 166 up
GSE1791_CTRL_VS_NEUROMEDINU_IN_T_CELL_LINE_6H_UP 0.0028653 0.01013 0.5434117 2.788647 0 187 up
GSE42021_TCONV_PLN_VS_TREG_PRECURSORS_THYMUS_DN 0.0028490 0.01013 0.5330160 2.763932 0 196 up 
fgp <- fgsea_plot(fgsea_res$res_msg, pathways_title='All signatures', condition_name='PTGS2 low vs high')

kable(fgp)
pathway pval padj ES NES nMoreExtreme size state
HALLMARK_TNFA_SIGNALING_VIA_NFKB 0.0030864 0.0185283 0.6718790 3.469995 0 195 up
GSE9988_LOW_LPS_VS_CTRL_TREATED_MONOCYTE_UP 0.0030395 0.0185283 0.6626050 3.383800 0 178 up
GSE9988_LOW_LPS_VS_VEHICLE_TREATED_MONOCYTE_UP 0.0030303 0.0185283 0.6476534 3.295345 0 175 up
GSE14769_UNSTIM_VS_80MIN_LPS_BMDM_DN 0.0030675 0.0185283 0.6270112 3.226162 0 191 up
PHONG_TNF_TARGETS_UP 0.0025189 0.0185283 0.7491497 3.217832 0 62 up
GSE9988_LPS_VS_CTRL_TREATED_MONOCYTE_UP 0.0030303 0.0185283 0.6315690 3.213505 0 175 up
GSE9988_LPS_VS_VEHICLE_TREATED_MONOCYTE_UP 0.0030120 0.0185283 0.6278268 3.191632 0 176 up
GSE14769_UNSTIM_VS_60MIN_LPS_BMDM_DN 0.0031250 0.0185283 0.6206065 3.190041 0 192 up
GSE30971_CTRL_VS_LPS_STIM_MACROPHAGE_WBP7_KO_4H_UP 0.0030488 0.0185283 0.6155783 3.154019 0 181 up
ANASTASSIOU_MULTICANCER_INVASIVENESS_SIGNATURE 0.0025189 0.0185283 0.7340077 3.152793 0 62 up
GSE9988_ANTI_TREM1_VS_ANTI_TREM1_AND_LPS_MONOCYTE_DN 0.0029674 0.0185283 0.6165688 3.133817 0 172 up
GSE29617_CTRL_VS_DAY7_TIV_FLU_VACCINE_PBMC_2008_UP 0.0030488 0.0185283 0.6123084 3.131190 0 180 up
GSE30971_CTRL_VS_LPS_STIM_MACROPHAGE_WBP7_KO_2H_UP 0.0030581 0.0185283 0.6108320 3.118400 0 179 up
HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION 0.0031153 0.0185283 0.6070484 3.104894 0 189 up
CHEN_HOXA5_TARGETS_9HR_UP 0.0031949 0.0185283 0.5944775 3.078689 0 211 up
GSE21678_WT_VS_FOXO1_FOXO3_KO_TREG_UP 0.0030303 0.0185283 0.6032215 3.069269 0 175 up
ZWANG_CLASS_3_TRANSIENTLY_INDUCED_BY_EGF 0.0031056 0.0185283 0.5855923 3.060070 0 219 up
GABRIELY_MIR21_TARGETS 0.0032787 0.0185283 0.5621520 3.047059 0 271 up
STK33_NOMO_UP 0.0032787 0.0185283 0.5656353 3.046990 0 267 up
PICCALUGA_ANGIOIMMUNOBLASTIC_LYMPHOMA_DN 0.0027548 0.0185283 0.6237117 3.034484 0 128 up
GSE42021_TREG_PLN_VS_CD24INT_TREG_THYMUS_UP 0.0030864 0.0185283 0.5868058 3.030625 0 195 up
MODULE_47 0.0030960 0.0185283 0.5783163 3.018859 0 214 up
UZONYI_RESPONSE_TO_LEUKOTRIENE_AND_THROMBIN 0.0023641 0.0185283 0.8018658 3.018412 0 34 up
BROCKE_APOPTOSIS_REVERSED_BY_IL6 0.0028490 0.0185283 0.6128985 3.014087 0 140 up
GO_COTRANSLATIONAL_PROTEIN_TARGETING_TO_MEMBRANE 0.0024938 0.0185283 0.6543159 3.014020 0 94 up
GSE9988_ANTI_TREM1_AND_LPS_VS_CTRL_TREATED_MONOCYTES_UP 0.0030303 0.0185283 0.5888956 2.999346 0 177 up
GSE369_PRE_VS_POST_IL6_INJECTION_SOCS3_KO_LIVER_UP 0.0030488 0.0185283 0.5848084 2.990562 0 180 up
STK33_UP 0.0033003 0.0185283 0.5542519 2.984593 0 263 up
KEGG_RIBOSOME 0.0025253 0.0185283 0.6687799 2.976542 0 81 up
TURASHVILI_BREAST_LOBULAR_CARCINOMA_VS_DUCTAL_NORMAL_UP 0.0026596 0.0185283 0.6959614 2.969446 0 66 up 
y <- read.csv('/Users/senosam/Documents/Massion_lab/radiomics_summary/TMA36_CANARY_khushbu.csv')
vsd_mat <- t(ls_preprocessed$vsd_mat)
p_all <- ls_preprocessed$p_all
p_all <- cbind(p_all, 'SILA_S' = y$SILA_S[match(p_all$pt_ID, y$pt_ID)])
vsd_mat <- data.frame(cbind(vsd_mat, 'SILA_S'=p_all$SILA_S))
COX1 = "ENSG00000198804.2"
COX2 = "ENSG00000073756.7"

ggpubr::ggscatter(vsd_mat, x = "SILA_S", y = c(COX1, COX2),
          add = "reg.line", conf.int = TRUE, combine = TRUE, scales='free',
          cor.coef = TRUE, add.params = list(color = 'grey65'), expand = c(0, 0),
          cor.coeff.args = list(method = "spearman"),
          xlab = "SILA score", ylab = 'VST (gene expression)') +
          ggpubr::font("xy.text", size = 10) +
          ggpubr::font("xlab", size = 12, face = 'bold') +
          ggpubr::font("ylab", size = 12, face = 'bold') 
## `geom_smooth()` using formula 'y ~ x'